Description of Thesis Work
In this thesis, real-time image processing techniques will be developed to detect vulnerable road users (primarily pedestrians, but also considering cyclists etc).

The application of this work is to improve the safety of commercial vehicles with respect to vulnerable road users. It is often difficult for commercial vehicle drivers to clearly observe the entire region around the vehicle; hence some form of automatic assistance is desirable.

High level specifications of the system to be investigated include:
• Detect pedestrians from a camera mounted on a commercial vehicle, which is either stationary, or moving slowly
• Development in C++
• Hardware is a regular desktop PC
• System should operate offline on recorded videos, or even with live video feed (if time permits)

The task comprises the following:
• Reviewing state-of-the-art image classification and machine learning algorithms for pedestrian detection (Adaboost, SVM etc)
• Adapting/modifying existing algorithms to work on commercial vehicles
• Collecting image data to use for dector/classifier training
• Using C++ and open source software such as OpenCV, design and develop a prototype system running in real time
• Perform some investigations to evaluate performance of the system